Papers with source models
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision (2021.acl-long)
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| Challenge: | Existing work on cross-lingual transfer learning focuses on transferring knowledge from high-resource languages to low-resourced ones. |
| Approach: | They propose a multi-view framework that integrates multiple source models into an aggregated source view and transfers it to a target view based on a task-specific model. |
| Outcome: | The proposed framework improves on three structured prediction tasks on 16 datasets. |
Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)
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| Challenge: | Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains. |
| Approach: | They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels. |
| Outcome: | The proposed framework outperforms state-of-the-art systems on 21 datasets. |
Cross-lingual Transfer Learning for Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing studies on English GEC have focused on improving it, but the resources required to train the models are not sufficient. |
| Approach: | They investigate cross-lingual transfer learning in grammatical error correction tasks . similarities between these languages is a key factor for successfully transferring grammatikal knowledge . |
| Outcome: | The proposed methods improve accuracy of grammatical error correction tasks in English and Russian, but lack the resources to train models in these languages. |
Single-dataset Experts for Multi-dataset Question Answering (2021.emnlp-main)
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| Challenge: | Prior work has focused on training one network on multiple datasets to build a model that performs well on all of the training datasets and generalizes and transfers better to new datasets. |
| Approach: | They combine multiple reading comprehension datasets to build a multi-dataset question answering model with an ensemble of single-data set experts. |
| Outcome: | The proposed model outperforms baseline models in in-distribution accuracy and generalization and transfer performance. |
Asymmetric Mutual Learning for Multi-source Unsupervised Sentiment Adaptation with Dynamic Feature Network (2022.coling-1)
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| Challenge: | Recent work on pre-trained language models (PrLMs) on labeled sentiment datasets has shown significant improvements on widerange of NLP tasks, including sentiment classification. |
| Approach: | They propose a multi-source unsupervised sentiment adaptation problem with pre-trained features to exploit the extracted pre-train features for efficient domain adaptation. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on multiple sentiment benchmarks and extensive ablation studies to verify the effectiveness of each module. |
From Text to Source: Results in Detecting Large Language Model-Generated Content (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) generate human-like text, but have ethical and misuse concerns. |
| Approach: | They evaluate whether a classifier trained to distinguish between source and target LLMs can detect text from an LLM without further training. |
| Outcome: | The proposed method detects text from target LLMs without further training. |
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation (2025.acl-long)
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Peiwen Yuan, Yueqi Zhang, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Existing efficient methods estimate performance of models on large benchmarks, but these methods rely on the assumption that target models have high prediction consistency with source models. |
| Approach: | They propose a method that conducts customized evaluation tailored to each target model. |
| Outcome: | The proposed method reduces the MAE of estimates by 31.4% on benchmarks across 300 models. |
Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness (2024.emnlp-main)
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| Challenge: | Existing zero-shot detection paradigms that use token cohesiveness are not available for large language models. |
| Approach: | They propose a generic dual-channel detection paradigm that uses token cohesiveness as a plug-and-play module to improve existing zero-shot detectors. |
| Outcome: | The proposed model is able to detect human-like text in black-box environments. |
Composable Cross-prompt Essay Scoring by Merging Models (2025.emnlp-main)
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| Challenge: | Existing approaches to cross-prompt automated essay scoring use all available sources . however, using multiple sources for continual adaptation raises privacy concerns . |
| Approach: | They propose a source-free adaptation approach that selectively merges the parameters of individual models without further access to the source datasets. |
| Outcome: | The proposed method outperforms joint-training methods on all sources while maintaining computational efficiency. |
Zero-Shot Detection of LLM-Generated Text using Temperature Sensitivity (2026.acl-long)
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| Challenge: | Existing methods for detecting LLM-generated text rely on statistical features that are insufficient for reliable detection. |
| Approach: | They propose a temperature-sensitive detector that modulates decoding temperature and monitors how probability distributions respond to temperature. |
| Outcome: | The proposed method is based on a temperature sensitivity feature and a simple zero-shot detector built upon normalized temperature sensitivity. |
Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)
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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang
| Challenge: | Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. |
| Approach: | They propose a model merging framework that modulates the contribution of each source model. |
| Outcome: | Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages. |